The present invention relates to a process for determining the flight configurations of an aircraft, especially a helicopter, as well as to a device for applying said process.
Within the framework of the present invention, flight configuration of an aircraft is understood to mean a flight phase, for example takeoff, landing or level flight, exhibiting determined flight characteristics, and capable of being differentiated from other flight phases, and during which the aircraft is subjected to relatively constant types of stresses.
It is often necessary to know the flight configurations of the aircraft, for example current flight configuration:
either for the purpose of real-time monitoring of the aircraft; PA1 or for the purpose of maintenance, for example to determine whether a particular member has been subjected to high stresses requiring its replacement. PA1 in that, in a preliminary step: PA1 in that, for a flight phase of said aircraft to be defined: PA1 the positions of the flight phases representative of said flight configuration are determined in said metric system from the measured values of said parameters; PA1 the center of gravity of the positions thus determined, corresponding to the center of gravity of said flight configuration, is calculated in said metric system from these positions; PA1 the distance between said center of gravity and the position of said flight phase is determined for each of said representative flight phases, in said metric system; PA1 the membership function in said flight configuration is determined, for each of said flight phases, from the corresponding distance thus determined; and PA1 said fuzziness index is calculated from the membership functions thus determined. PA1 L is the number of relevant flight phases representative of said flight configuration, PA1 .mu.(Xj) represents the membership function of a flight phase in said flight configuration, and PA1 S(x) is Shannon's entropy function, defined by: EQU S(x)=-x.Log(x)-(1-x).Log(1-x). PA1 the distance between said flight phase and the center of gravity of the relevant flight configuration is calculated in said metric system, for all the defined flight configurations; PA1 the distances thus calculated are compared; and PA1 the n flight configurations with the smallest distances are picked. PA1 R is the position of the center of gravity of said flight configuration in said metric system, PA1 t is a predefined coefficient, and PA1 .sigma. represents the diagonal matrix of variances of said flight configuration. PA1 those among the n closest flight configurations are sought for which the membership function of the flight phase is greater than or equal to the product K..nu.(Ci), in which K represents a coefficient less than or equal to 1, preferably equal to 0.85, and .nu.(Ci) is the calculated fuzziness index; and PA1 we deduce: PA1 a first computer capable of calculating the fuzziness index of each of the defined flight configurations; PA1 a second computer capable of calculating the membership functions of a flight phase to be defined in the n closest flight configurations; and PA1 a central computer linked to said first and second computers and capable of determining the flight configuration of which said flight phase to be defined forms part.
Various methods for determining the flight configurations of an aircraft are known.
Firstly, methods are known which apply neural networks which determine the flight configurations from measurements made in-flight and from stored predetermined coefficients. The phase of determining and storing these coefficients is lengthy and cumbersome. Moreover, once set up, these methods can be very difficult to modify, for example in order to be adapted to new knowledge or to technical modifications of the aircraft.
Secondly, a method is known which is based on artificial intelligence and which essentially uses empirical data. This method also requires lengthy application and does not allow all the possible flight configurations to be recognized.
A method may also be cited which is based on analyzing data, in particular specific flight parameters, which method does not however afford completely satisfactory accuracy.